End-to-end reproducible AI pipelines in radiology using the cloud

D Bontempi, L Nuernberg, S Pai… - Nature …, 2024 - nature.com
Artificial intelligence (AI) algorithms hold the potential to revolutionize radiology. However, a
significant portion of the published literature lacks transparency and reproducibility, which …

Foundation Models in Radiology: What, How, When, Why and Why Not

M Paschali, Z Chen, L Blankemeier, M Varma… - arXiv preprint arXiv …, 2024 - arxiv.org
Recent advances in artificial intelligence have witnessed the emergence of large-scale deep
learning models capable of interpreting and generating both textual and imaging data. Such …

A Comprehensive Survey of Foundation Models in Medicine

W Khan, S Leem, KB See, JK Wong, S Zhang… - arXiv preprint arXiv …, 2024 - arxiv.org
Foundation models (FMs) are large-scale deep-learning models trained on extensive
datasets using self-supervised techniques. These models serve as a base for various …

Machine learning enabled prediction of digital biomarkers from whole slide histopathology images

ZR McCaw, A Shcherbina, Y Shah, D Huang, S Elliott… - medRxiv, 2024 - medrxiv.org
Current predictive biomarkers generally leverage technologies such as immunohis-
tochemistry or genetic analysis, which may require specialized equipment, be time-intensive …

Prognostic value of FDX1, the cuprotosis key gene, and its prediction models across imaging modalities and histology

Q Yue, M Zhang, W Jiang, L Gao, R Ye, J Hong, Y Li - BMC cancer, 2024 - Springer
Background Cuprotosis has been identified as a novel way of cell death. The key regulator
ferredoxin 1 (FDX1) was explored via pan-cancer analysis, and its prediction models were …